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Course Outline
Module 1: Context, Scope and Delivery Challenges
- Distinguishing between autocomplete functionality and autonomous multi-step execution
- Addressing common misconceptions regarding AI in software delivery
- Rationale for the insufficiency of prompt engineering alone
- Assessment of participant tooling, operational pain points, and strategic objectives
- Selection of appropriate AI operating models for engineering teams
Module 2: Specification Ingestion and Structured Decomposition
- Establishing a structural inventory of stakeholder documentation
- Techniques for requirement extraction
- Implementation of chunking strategies: structural, semantic, and sliding-window approaches
- Maintenance of dependencies and cross-references
- Processing of tables, diagrams, flowcharts, and mixed-input formats
- Effective management of context windows
Module 3: Human Judgment Boundaries
- Identification of decision points requiring human oversight
- Detection of hallucinated dependencies
- Recognition of fabricated constraints and inverted logic
- Mitigation of unsafe automated assistance defaults
- Implementation of validation frameworks to ensure traceability, consistency, and completeness
Module 4: From Requirements to Code with Agentic Tools
- Adoption of an architecture-first delivery model
- Component mapping and definition of service boundaries
- Utilization of API contracts as central delivery anchors
- Enforcement of persistent rules and constraints within AI tools
- Alignment of task instructions with established requirements
- Comparison of minimal prompting versus constrained prompting methodologies
- Execution of contract-first generation for backend and frontend systems
Module 5: Agentic Iteration Loop
- Implementation of self-correction mechanisms
- Execution of controlled iterative delivery cycles
- Review procedures for code diffs and modifications
- Identification of scope creep and unauthorized changes
- Management of limited context memory constraints
- Leveraging iteration history to drive continuous improvement
Module 6: Code Quality Enforcement
- Application of prompt constraints for edge-case scenarios
- Maintenance of rules documents as dynamic governance artifacts
- Deployment of automated gates using linting and static analysis tools
- Integration of security scanning within AI-generated code workflows
- Verification of dependency and architectural conformance
- Establishment of human review protocols for AI outputs
Module 7: Feedback Loops and Continuous Improvement
- Integration of structured failure data back into AI workflows
- Determination of bounded iterations and stop criteria
- Documentation of cycles and operational outcomes
- Refinement of rules documents over time
- Development of reusable engineering intelligence resources
Module 8: Security Anti-Patterns in AI Delivery
- Identification of common security risks associated with generated code
- Utilization of technology-specific security rules appendices
- Implementation of pre-commit security scanning
- Enforcement of secure SDLC controls for AI-assisted development
- Maintenance of human accountability in secure delivery processes
Module 9: Testing Anchored to Specifications
- Generation of test specifications derived from requirements
- Design of domain-language tests
- Safe generation of test implementations
- Application of mutation testing concepts
- Validation of specification coverage
- Review of assertion strength
- Utilization of diagnostic questioning models
Module 10: Maintaining the System
- Maintenance of living artifacts: contracts, maps, rules, and test specifications
- Evolving constraints to reflect changing requirements
- Implementation of AI governance strategies for long-term maintainability
- Prediction of technical debt through the application of AI controls
- Establishment of an operating model for sustainable AI engineering teams
Requirements
The following qualifications are recommended for applicants:
- Demonstrated involvement in software development initiatives
- Comprehensive understanding of foundational application architecture principles
- Proficiency with APIs, backend or frontend environments, and full-stack deployment methodologies
- Familiarity with Agile frameworks or iterative delivery cycles
- Working knowledge of standard software testing protocols
- Previous exposure to artificial intelligence-based coding utilities is advantageous but not required
- Appropriate for mid-to-senior level technical experts seeking roles tailored for government professionals
14 Hours
